Project Summary One of the most fundamental questions in brain aging research is whether age-related alterations affect all brain regions equally, or whether some regions, and cell types within those regions, are more vulnerable to the effects of aging than others. Aging is associated with cognitive decline, and is reported to cause alterations in a variety of important cellular processes and in a variety of cell types (e.g., microglia, astrocytes, neurons). Broad classes of cells in affected brain regions are known to be selectively vulnerable to age-related neurodegenerative diseases, but the specific molecular mechanisms underlying this vulnerability are unclear. An essential prerequisite to understanding this selective vulnerability is to understand the detailed changes at cell type and circuit levels during the aging process. Cataloging brain cell types and their connectivity in normal aging brain is foundational to uncovering the mechanisms and therapeutic opportunities for age-related brain disorders. State-of-the-art single-cell technologies, in particular single-cell transcriptomics with its high dimensional molecular information, but also spatial transcriptomics, single-cell epigenomics and single-cell morphology, are providing transformative information about brain cell types at an unprecedented scale and resolution. We propose to utilize our well-established omics pipelines to characterize and classify cell types in 18 months old male and female C57BL/6J mice and compare the results with the extensive brain-wide datasets in young adult (~P56) mice already being generated in the current BRAIN Initiative Cell Census Network (BICCN). We will use single-nucleus transcriptomics and epigenomics to obtain a high-level survey of neuronal and non-neuronal cell classes/types across the entire mouse brain, and then an in-depth single-cell and spatial transcriptomic study in brain areas showing age-related changes and/or vulnerable to neurodegenerative diseases. We will utilize our imaging?based registration process to map all data into the Common Coordinate Framework (CCF), which allows accurate cross-age quantitative comparisons that will be crucial for uncovering age-related changes. By conducting concurrent single-cell gene expression and chromatin accessibility measurements in the same brain regions, and a detailed spatial transcriptomic map of the proportion and distribution of different cell types and specific molecular pathways, we will chart an integrated path towards gaining mechanistic insight underlying the cognitive decline in aging and age-related disease pathology.